From fe387094e45c3a7275df20c04829ee565d9e1af8 Mon Sep 17 00:00:00 2001
From: unknown <ana.santana.ironhack@gmail.com>
Date: Thu, 10 Aug 2023 23:44:05 +0100
Subject: [PATCH] lab done

---
 .../.ipynb_checkpoints/main-checkpoint.ipynb  | 1829 +++++++++++++++++
 {data => your-code/data}/ages_population.csv  |    0
 {data => your-code/data}/ages_population2.csv |    0
 {data => your-code/data}/ages_population3.csv |    0
 .../data}/roll_the_dice_hundred.csv           |    0
 .../data}/roll_the_dice_thousand.csv          |    0
 your-code/main.ipynb                          | 1439 ++++++++++++-
 7 files changed, 3202 insertions(+), 66 deletions(-)
 create mode 100644 your-code/.ipynb_checkpoints/main-checkpoint.ipynb
 rename {data => your-code/data}/ages_population.csv (100%)
 rename {data => your-code/data}/ages_population2.csv (100%)
 rename {data => your-code/data}/ages_population3.csv (100%)
 rename {data => your-code/data}/roll_the_dice_hundred.csv (100%)
 rename {data => your-code/data}/roll_the_dice_thousand.csv (100%)

diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
new file mode 100644
index 0000000..e7e2a8e
--- /dev/null
+++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
@@ -0,0 +1,1829 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Libraries\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import random\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 1\n",
+    "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
+    "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 6, 3, 1, 6, 3, 2, 2, 1, 2]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def roll_dice_trials(k):\n",
+    "    mylist = np.arange(1, 7)\n",
+    "    return random.choices(mylist, k=k)\n",
+    "\n",
+    "dice_10 = roll_dice_trials(10)\n",
+    "dice_10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Results</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Trials</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Results\n",
+       "Trials         \n",
+       "1             5\n",
+       "2             6\n",
+       "3             3\n",
+       "4             1\n",
+       "5             6\n",
+       "6             3\n",
+       "7             2\n",
+       "8             2\n",
+       "9             1\n",
+       "10            2"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice_df = pd.DataFrame(dice_10, columns=[\"Results\"])\n",
+    "dice_df.index = range(1, len(dice_df) + 1)\n",
+    "dice_df.index.name = \"Trials\"\n",
+    "dice_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "dice = np.sort(dice_df)\n",
+    "plt.hist(dice_df)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method IndexOpsMixin.value_counts of Trials\n",
+       "1     5\n",
+       "2     6\n",
+       "3     3\n",
+       "4     1\n",
+       "5     6\n",
+       "6     3\n",
+       "7     2\n",
+       "8     2\n",
+       "9     1\n",
+       "10    2\n",
+       "Name: Results, dtype: int32>"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "freq= dice_df[\"Results\"].value_counts\n",
+    "freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Ana\\AppData\\Local\\Temp\\ipykernel_9896\\400507941.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(freq)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(freq)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 2\n",
+    "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
+    "\n",
+    "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Results</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Trials</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Results\n",
+       "Trials         \n",
+       "1             5\n",
+       "2             6\n",
+       "3             3\n",
+       "4             1\n",
+       "5             6\n",
+       "6             3\n",
+       "7             2\n",
+       "8             2\n",
+       "9             1\n",
+       "10            2"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "dice_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(3+6+6+2+4+4+4+4+6+1)/10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.1"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def cal_mean(column):\n",
+    "    return sum(column)/len(column)\n",
+    "\n",
+    "cal_mean(dice_df[\"Results\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1, 2, 3, 5, 6]), array([2, 3, 2, 1, 2], dtype=int64))"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "fq = np.unique(dice_df, return_counts=True)\n",
+    "unique_values = fq[0]\n",
+    "frequencies = fq[1]\n",
+    "fq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.1"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_values = np.sum(frequencies)\n",
+    "sum_products = np.sum(unique_values * frequencies)\n",
+    "\n",
+    "mean = sum_products / total_values\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
+    "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "def calculate_median(data):\n",
+    "    sorted_data = np.sort(data)\n",
+    "    num_observations = len(sorted_data)\n",
+    "\n",
+    "    if num_observations % 2 == 1:  \n",
+    "        middle_index = num_observations // 2\n",
+    "        median = sorted_data[middle_index]\n",
+    "    else:  # Even number of observations\n",
+    "        middle_index_1 = (num_observations - 1) // 2\n",
+    "        middle_index_2 = num_observations // 2\n",
+    "        median = (sorted_data[middle_index_1] + sorted_data[middle_index_2]) / 2\n",
+    "\n",
+    "    return median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4.5])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calculate_median(dice_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here\n",
+    "def calculate_quartiles(data):\n",
+    "    sorted_data = np.sort(data)\n",
+    "    num_observations = len(sorted_data)\n",
+    "    q1_index = int(num_observations * 0.25)\n",
+    "    \n",
+    "    q1 = calculate_median(sorted_data[:q1_index + 1])\n",
+    "    \n",
+    "    q2 = calculate_median(sorted_data)\n",
+    "    \n",
+    "    q3_index = int(num_observations * 0.75)\n",
+    "    \n",
+    "    q3 = calculate_median(sorted_data[q3_index:])\n",
+    "\n",
+    "    return q1, q2, q3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([6]), array([4.5]), array([1]))"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = dice_df\n",
+    "calculate_quartiles(dice_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 3\n",
+    "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
+    "#### 1.- Sort the values and plot them. What do you see?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>95</td>\n",
+       "      <td>95</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>96</td>\n",
+       "      <td>96</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>97</td>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>98</td>\n",
+       "      <td>98</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  roll  value\n",
+       "0            0     0      1\n",
+       "1            1     1      2\n",
+       "2            2     2      6\n",
+       "3            3     3      1\n",
+       "4            4     4      6\n",
+       "..         ...   ...    ...\n",
+       "95          95    95      4\n",
+       "96          96    96      6\n",
+       "97          97    97      1\n",
+       "98          98    98      3\n",
+       "99          99    99      6\n",
+       "\n",
+       "[100 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "roll_dice = pd.read_csv(\"data/roll_the_dice_hundred.csv\")\n",
+    "roll_dice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>47</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>56</td>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>73</td>\n",
+       "      <td>73</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>24</td>\n",
+       "      <td>24</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>21</td>\n",
+       "      <td>21</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  roll  value\n",
+       "0            0     0      1\n",
+       "47          47    47      1\n",
+       "56          56    56      1\n",
+       "9            9     9      1\n",
+       "73          73    73      1\n",
+       "..         ...   ...    ...\n",
+       "17          17    17      6\n",
+       "11          11    11      6\n",
+       "24          24    24      6\n",
+       "21          21    21      6\n",
+       "99          99    99      6\n",
+       "\n",
+       "[100 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_sort = roll_dice.sort_values(by = \"value\")\n",
+    "roll_sort"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Ana\\AppData\\Local\\Temp\\ipykernel_9896\\1384629514.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(roll_sort)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(roll_sort)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def cal_mean(column):\n",
+    "    return sum(column)/len(column)\n",
+    "\n",
+    "cal_mean(roll_dice[\"value\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6    23\n",
+       "4    22\n",
+       "2    17\n",
+       "3    14\n",
+       "1    12\n",
+       "5    12\n",
+       "Name: value, dtype: int64"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "fq = roll_dice[\"value\"].value_counts()\n",
+    "\n",
+    "fq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x2341fa105b0>"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "plt.hist(roll_dice[\"value\"])\n",
+    "x = data[\"value\"]\n",
+    "plt.axvline(x.mean(), color = \"pink\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>995</td>\n",
+       "      <td>995</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>996</td>\n",
+       "      <td>996</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>997</td>\n",
+       "      <td>997</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>998</td>\n",
+       "      <td>998</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>999</td>\n",
+       "      <td>999</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  roll  value\n",
+       "0             0     0      5\n",
+       "1             1     1      6\n",
+       "2             2     2      1\n",
+       "3             3     3      6\n",
+       "4             4     4      5\n",
+       "..          ...   ...    ...\n",
+       "995         995   995      1\n",
+       "996         996   996      4\n",
+       "997         997   997      4\n",
+       "998         998   998      3\n",
+       "999         999   999      6\n",
+       "\n",
+       "[1000 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "thousand = pd.read_csv(\"data/roll_the_dice_thousand.csv\")\n",
+    "thousand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6    23\n",
+       "4    22\n",
+       "2    17\n",
+       "3    14\n",
+       "1    12\n",
+       "5    12\n",
+       "Name: value, dtype: int64"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fq_thousand = roll_dice[\"value\"].value_counts()\n",
+    "fq_thousand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(fq_thousand.index, fq_thousand.values, color=\"lemonchiffon\")\n",
+    "plt.xlabel(\"Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 4\n",
+    "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
+    "\n",
+    "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>53.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     observation\n",
+       "0           68.0\n",
+       "1           12.0\n",
+       "2           45.0\n",
+       "3           38.0\n",
+       "4           49.0\n",
+       "..           ...\n",
+       "995         27.0\n",
+       "996         47.0\n",
+       "997         53.0\n",
+       "998         33.0\n",
+       "999         31.0\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_pop = pd.read_csv(\"data/ages_population.csv\")\n",
+    "ages_pop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39.0    45\n",
+       "41.0    36\n",
+       "30.0    34\n",
+       "35.0    33\n",
+       "43.0    32\n",
+       "        ..\n",
+       "73.0     1\n",
+       "82.0     1\n",
+       "70.0     1\n",
+       "71.0     1\n",
+       "69.0     1\n",
+       "Name: observation, Length: 72, dtype: int64"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "freq_ages = ages_pop[\"observation\"].value_counts()\n",
+    "freq_ages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(freq_ages.index, freq_ages.values, color=\"salmon\")\n",
+    "plt.xlabel(\"Age\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 36.56\n",
+      "Standard deviation: 12.816499625976762\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "mean = ages_pop[\"observation\"].mean()\n",
+    "print(f\"Mean: {mean}\")\n",
+    "\n",
+    "std_dev = ages_pop[\"observation\"].std()\n",
+    "print(f\"Standard deviation: {std_dev}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_pop2= pd.read_csv(\"data/ages_population2.csv\")\n",
+    "ages_pop2_freq= ages_pop2[\"observation\"].value_counts()\n",
+    "\n",
+    "plt.bar(ages_pop2_freq.index, ages_pop2_freq.values)\n",
+    "plt.xlabel(\"Age\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean 1: 36.56\n",
+      "Standard deviation 1: 12.816499625976762\n",
+      "Mean 2:27.155\n",
+      "Standard deviation 2 :2.969813932689186\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "mean = ages_pop[\"observation\"].mean()\n",
+    "print(f\"Mean 1: {mean}\")\n",
+    "\n",
+    "std_dev = ages_pop[\"observation\"].std()\n",
+    "print(f\"Standard deviation 1: {std_dev}\")\n",
+    "\n",
+    "mean = ages_pop2[\"observation\"].mean()\n",
+    "print(f\"Mean 2:{mean}\")\n",
+    "\n",
+    "std_dev = ages_pop2[\"observation\"].std()\n",
+    "print(f\"Standard deviation 2 :{std_dev}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 5\n",
+    "Now is the turn of `ages_population3.csv`.\n",
+    "\n",
+    "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eUFZWlhYuXKikpCTNmjVLffv21b59+xQeHu6XOgAAgN18XljvzJkz2rRpk77++msNHTpU4eHh+vbbb9WkSRM1bty4RtdYu3at135OTo4iIyO1a9cu3XXXXTLGaP78+Zo+fbrS0tIkSYsWLVJUVJSWLFmiMWPGVLtmZWWlKisrPfvl5eW+tggAACzhU6A5cOCA+vXrp4MHD6qyslJ9+/ZVeHi4XnjhBZ06dUqvvvqqT8UcP35cktS0aVNJUlFRkUpLS5WSkuI5x+12q1evXtq6desFA01mZqZmzpzp09cHUHss8AYgEPh0D82ECROUnJyso0ePKiwszDM+ePBgffTRRz4VYoxRenq6evbsqfbt20uSSktLJUlRUVFe50ZFRXmOnW/q1Kk6fvy4ZysuLvapHgAAYA+fn3L6+OOPFRoa6jUeHx+vw4cP+1TI+PHjtWfPHm3ZsqXasfNXIjbGXHR1YrfbLbfb7VMNAADATj7N0FRVVens2bPVxg8dOuTTjbpPPfWUVq1apY0bN6p58+ae8ejoaEmqNhtTVlZWbdYGAABcu3wKNH379tX8+fM9+y6XSydPntSMGTNq9XEIxhiNHz9eK1as0IYNG5SQkOB1PCEhQdHR0crNzfWMnT59Wnl5eerRo4cvpQMAgCDk01tO//Ef/6E+ffqobdu2OnXqlIYOHarCwkI1a9ZMb7/9do2vM27cOC1ZskT/9V//pfDwcM9MTEREhMLCwuRyuTRx4kTNmTNHiYmJSkxM1Jw5c9SwYUMNHTrUl9IBAEAQ8inQxMbGKj8/X2+//bY++eQTVVVVafTo0frDH/7gdZPw5WRnZ0uSevfu7TWek5OjkSNHSpKeffZZ/fTTT3ryySd19OhRde3aVevXr2cNGgAA4OHzOjRhYWEaNWqURo0a5fMXr8lnQblcLmVkZCgjI8PnrwMAAIKbT4Fm8eLFlzw+fPhwn4oBAADwhU+BZsKECV77P//8s3788UeFhoaqYcOGBBoAAFCnfAo0R48erTZWWFioJ554Qn/84x+vuCggGJy/gq4vq+eyCi8QWK72v0l//N64Vvn02PaFJCYmau7cudVmbwAAAK42vwUaSapXr56+/fZbf14SAADgsnx6y2nVqlVe+8YYlZSU6OWXX9Ydd9zhl8IAAABqyqdAM2jQIK99l8ulm266SXfffbdefPFFf9QFAABQYz4FmqqqKn/XAQAA4DO/3kMDAADgBJ9maNLT02t8blZWli9fAgAAoMZ8CjS7d+/WJ598ojNnzqh169aSpC+//FL16tXTbbfd5jnP5XL5p0oAAIBL8CnQDBw4UOHh4Vq0aJFuuOEGSb8stvfoo4/qzjvv1KRJk/xaJABvLLh37eBnDdSMT/fQvPjii8rMzPSEGUm64YYbNGvWLJ5yAgAAdc6nQFNeXq7vvvuu2nhZWZlOnDhxxUUBAADUhk+BZvDgwXr00Uf13nvv6dChQzp06JDee+89jR49Wmlpaf6uEQAA4JJ8uofm1Vdf1eTJkzVs2DD9/PPPv1woJESjR4/WvHnz/FogAADA5fgUaBo2bKhXXnlF8+bN09dffy1jjFq1aqVGjRr5uz4AAIDLuqKF9UpKSlRSUqKkpCQ1atRIxhh/1QUAAFBjPgWaI0eO6J577lFSUpL69++vkpISSdJjjz3GI9sAAKDO+RRonnnmGdWvX18HDx5Uw4YNPeNDhgzR2rVr/VYcAABATfh0D8369eu1bt06NW/e3Gs8MTFRBw4c8EthAAAANeVToKmoqPCamTnn+++/l9vtvuKigGBl86qvNtfuLzZ/D2yu3WYX+77z8/A/n95yuuuuu7R48WLPvsvlUlVVlebNm6c+ffr4rTgAAICa8GmGZt68eerdu7d27typ06dP69lnn9XevXv1ww8/6OOPP/Z3jQAAAJfk0wxN27ZttWfPHt1+++3q27evKioqlJaWpt27d+uWW27xd40AAACXVOsZmp9//lkpKSl67bXXNHPmzKtREwAAQK3Ueoamfv36+uyzz+Ryua5GPQAAALXm01tOw4cP14IFC/xdCwAAgE98uin49OnTeuONN5Sbm6vk5ORqn+GUlZXll+IAAABqolaB5ptvvlHLli312Wef6bbbbpMkffnll17n8FYUAACoa7UKNImJiSopKdHGjRsl/fJRB3/5y18UFRV1VYoDAACoiVrdQ3P+p2l/8MEHqqio8GtBAAAAteXTTcHnnB9wAAAAnFCrQONyuardI8M9MwAAwGm1uofGGKORI0d6PoDy1KlTGjt2bLWnnFasWOG/CgEAAC6jVoFmxIgRXvvDhg3zazEAAAC+qFWgycnJ8esX37x5s+bNm6ddu3appKREK1eu1KBBgzzHR44cqUWLFnm9pmvXrtq2bZtf6wAAAHa7opuCr1RFRYU6deqkl19++aLn9OvXTyUlJZ5tzZo1dVghAACwgU8rBftLamqqUlNTL3mO2+1WdHR0HVUEAABs5OgMTU1s2rRJkZGRSkpK0uOPP66ysrJLnl9ZWany8nKvDQAABDdHZ2guJzU1VQ8++KDi4+NVVFSk559/Xnfffbd27drledLqfJmZmZo5c2YdVwpcmZZTVlcb2z93gAOVoC6c//PmZx14+Ddpn4AONEOGDPH8uX379kpOTlZ8fLxWr16ttLS0C75m6tSpSk9P9+yXl5crLi7uqtcKAACcE9CB5nwxMTGKj49XYWHhRc9xu90Xnb0BAADBKeDvofm1I0eOqLi4WDExMU6XAgAAAoijMzQnT57UV1995dkvKipSfn6+mjZtqqZNmyojI0MPPPCAYmJitH//fk2bNk3NmjXT4MGDHawaAAAEGkcDzc6dO9WnTx/P/rl7X0aMGKHs7GwVFBRo8eLFOnbsmGJiYtSnTx8tW7ZM4eHhTpUMAAACkKOBpnfv3pf8xO5169bVYTUAAMBWVt1DAwAAcCEEGgAAYD0CDQAAsJ5V69AAdYEVQq8dwfizrm1Pwfg9wLWJGRoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANYj0AAAAOsRaAAAgPUINAAAwHosrAcAqLHzF+JjET4ECmZoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1WCkYQe/8lU0lVjcFrkUXWuWY3w/BgxkaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1CDQAAMB6LKyHa1ZtF9S62PkXWqwLAPyJ3zOXxwwNAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrORpoNm/erIEDByo2NlYul0vvv/++13FjjDIyMhQbG6uwsDD17t1be/fudaZYAAAQsBwNNBUVFerUqZNefvnlCx5/4YUXlJWVpZdfflk7duxQdHS0+vbtqxMnTtRxpQAAIJA5ug5NamqqUlNTL3jMGKP58+dr+vTpSktLkyQtWrRIUVFRWrJkicaMGVOXpQIAgAAWsPfQFBUVqbS0VCkpKZ4xt9utXr16aevWrRd9XWVlpcrLy702AAAQ3AJ2peDS0lJJUlRUlNd4VFSUDhw4cNHXZWZmaubMmVe1NjiPVTOBwFHbVbeBqyFgZ2jOcblcXvvGmGpjvzZ16lQdP37csxUXF1/tEgEAgMMCdoYmOjpa0i8zNTExMZ7xsrKyarM2v+Z2u+V2u696fQAAIHAE7AxNQkKCoqOjlZub6xk7ffq08vLy1KNHDwcrAwAAgcbRGZqTJ0/qq6++8uwXFRUpPz9fTZs2VYsWLTRx4kTNmTNHiYmJSkxM1Jw5c9SwYUMNHTrUwaoBAECgcTTQ7Ny5U3369PHsp6enS5JGjBihhQsX6tlnn9VPP/2kJ598UkePHlXXrl21fv16hYeHO1UyAAAIQI4Gmt69e8sYc9HjLpdLGRkZysjIqLuiAACAdQL2HhoAAICaItAAAADrBexj24BUuwX0WNwL8B8Wr4RtmKEBAADWI9AAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANZjpWAEBFYlBYJPbVfvZrXv2uH75Y0ZGgAAYD0CDQAAsB6BBgAAWI9AAwAArEegAQAA1iPQAAAA6xFoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALBeiNMFAACuLS2nrK42tn/ugFqfX9vrXCuu1e8XMzQAAMB6BBoAAGA9Ag0AALAegQYAAFiPQAMAAKwX0IEmIyNDLpfLa4uOjna6LAAAEGAC/rHtdu3a6cMPP/Ts16tXz8FqAABAIAr4QBMSElKrWZnKykpVVlZ69svLy69GWQAAIIAEfKApLCxUbGys3G63unbtqjlz5ujmm2++6PmZmZmaOXNmHVaI2gj2hZ0AAM4I6HtounbtqsWLF2vdunV6/fXXVVpaqh49eujIkSMXfc3UqVN1/Phxz1ZcXFyHFQMAACcE9AxNamqq588dOnRQ9+7ddcstt2jRokVKT0+/4GvcbrfcbnddlQgAAAJAQM/QnK9Ro0bq0KGDCgsLnS4FAAAEEKsCTWVlpb744gvFxMQ4XQoAAAggAR1oJk+erLy8PBUVFemf//ynfv/736u8vFwjRoxwujQAABBAAvoemkOHDunhhx/W999/r5tuukndunXTtm3bFB8f73RpAAAggAR0oFm6dKnTJQAAAAsE9FtOAAAANUGgAQAA1gvot5wQ+Fj5FwAQCJihAQAA1iPQAAAA6xFoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsx8J6AICgcv6Cnyz2eW1ghgYAAFiPQAMAAKxHoAEAANYj0AAAAOsRaAAAgPUINAAAwHoEGgAAYD0CDQAAsB6BBgAAWI+Vgq+C81eplC69UmVtz7+aLlaLzT0BAC7uSn+/B8rvdmZoAACA9Qg0AADAegQaAABgPQINAACwHoEGAABYj0ADAACsR6ABAADWI9AAAADrsbDeFbqaCwz5a7Gj2o4DAGDbAqnM0AAAAOsRaAAAgPUINAAAwHoEGgAAYD0CDQAAsJ4VgeaVV15RQkKCGjRooM6dO+sf//iH0yUBAIAAEvCBZtmyZZo4caKmT5+u3bt3684771RqaqoOHjzodGkAACBABHygycrK0ujRo/XYY4/pt7/9rebPn6+4uDhlZ2c7XRoAAAgQAb2w3unTp7Vr1y5NmTLFazwlJUVbt2694GsqKytVWVnp2T9+/Lgkqby8/KrUWFX5o9d+eXl5tbHLff2LnX+l1zl3bm3Ga1vL1RoP1toDpUZqD7waba49UGqkdmdr9Ldz1zXG1OwFJoAdPnzYSDIff/yx1/js2bNNUlLSBV8zY8YMI4mNjY2NjY0tCLbi4uIaZYaAnqE5x+Vyee0bY6qNnTN16lSlp6d79quqqvTDDz/oxhtvvOhraqq8vFxxcXEqLi5WkyZNruhage5a6fVa6VOi12B1rfR6rfQp0es5xhidOHFCsbGxNbpWQAeaZs2aqV69eiotLfUaLysrU1RU1AVf43a75Xa7vcauv/56v9bVpEmToP9Lds610uu10qdEr8HqWun1WulToldJioiIqPE1Avqm4NDQUHXu3Fm5uble47m5uerRo4dDVQEAgEAT0DM0kpSenq5HHnlEycnJ6t69u/72t7/p4MGDGjt2rNOlAQCAABHwgWbIkCE6cuSI/vznP6ukpETt27fXmjVrFB8fX+e1uN1uzZgxo9pbWsHoWun1WulTotdgda30eq30KdGrr1zG1PR5KAAAgMAU0PfQAAAA1ASBBgAAWI9AAwAArEegAQAA1iPQ1NArr7yihIQENWjQQJ07d9Y//vEPp0u6Yps3b9bAgQMVGxsrl8ul999/3+u4MUYZGRmKjY1VWFiYevfurb179zpT7BXKzMxUly5dFB4ersjISA0aNEj79u3zOicY+s3OzlbHjh09i1R1795dH3zwged4MPR4MZmZmXK5XJo4caJnLFj6zcjIkMvl8tqio6M9x4OlT0k6fPiwhg0bphtvvFENGzbUv/zLv2jXrl2e48HSa8uWLav9TF0ul8aNGycpePqUpDNnzuhPf/qTEhISFBYWpptvvll//vOfVVVV5TnHL/369CFL15ilS5ea+vXrm9dff918/vnnZsKECaZRo0bmwIEDTpd2RdasWWOmT59uli9fbiSZlStXeh2fO3euCQ8PN8uXLzcFBQVmyJAhJiYmxpSXlztT8BW47777TE5Ojvnss89Mfn6+GTBggGnRooU5efKk55xg6HfVqlVm9erVZt++fWbfvn1m2rRppn79+uazzz4zxgRHjxeyfft207JlS9OxY0czYcIEz3iw9DtjxgzTrl07U1JS4tnKyso8x4Olzx9++MHEx8ebkSNHmn/+85+mqKjIfPjhh+arr77ynBMsvZaVlXn9PHNzc40ks3HjRmNM8PRpjDGzZs0yN954o/mf//kfU1RUZN59913TuHFjM3/+fM85/uiXQFMDt99+uxk7dqzXWJs2bcyUKVMcqsj/zg80VVVVJjo62sydO9czdurUKRMREWFeffVVByr0r7KyMiPJ5OXlGWOCu98bbrjBvPHGG0Hb44kTJ0xiYqLJzc01vXr18gSaYOp3xowZplOnThc8Fkx9Pvfcc6Znz54XPR5MvZ5vwoQJ5pZbbjFVVVVB1+eAAQPMqFGjvMbS0tLMsGHDjDH++7nyltNlnD59Wrt27VJKSorXeEpKirZu3epQVVdfUVGRSktLvfp2u93q1atXUPR9/PhxSVLTpk0lBWe/Z8+e1dKlS1VRUaHu3bsHZY+SNG7cOA0YMED33nuv13iw9VtYWKjY2FglJCTooYce0jfffCMpuPpctWqVkpOT9eCDDyoyMlK33nqrXn/9dc/xYOr1106fPq0333xTo0aNksvlCro+e/bsqY8++khffvmlJOnTTz/Vli1b1L9/f0n++7kG/ErBTvv+++919uzZah+GGRUVVe1DM4PJud4u1PeBAwecKMlvjDFKT09Xz5491b59e0nB1W9BQYG6d++uU6dOqXHjxlq5cqXatm3r+cUQDD2es3TpUn3yySfasWNHtWPB9DPt2rWrFi9erKSkJH333XeaNWuWevToob179wZVn998842ys7OVnp6uadOmafv27Xr66afldrs1fPjwoOr1195//30dO3ZMI0eOlBRcf3cl6bnnntPx48fVpk0b1atXT2fPntXs2bP18MMPS/JfvwSaGnK5XF77xphqY8EoGPseP3689uzZoy1btlQ7Fgz9tm7dWvn5+Tp27JiWL1+uESNGKC8vz3M8GHqUpOLiYk2YMEHr169XgwYNLnpeMPSbmprq+XOHDh3UvXt33XLLLVq0aJG6desmKTj6rKqqUnJysubMmSNJuvXWW7V3715lZ2dr+PDhnvOCoddfW7BggVJTUxUbG+s1Hix9Llu2TG+++aaWLFmidu3aKT8/XxMnTlRsbKxGjBjhOe9K++Utp8to1qyZ6tWrV202pqysrFqaDCbnnqAItr6feuoprVq1Shs3blTz5s0948HUb2hoqFq1aqXk5GRlZmaqU6dOeumll4KqR0natWuXysrK1LlzZ4WEhCgkJER5eXn6y1/+opCQEE9PwdLvrzVq1EgdOnRQYWFhUP1cY2Ji1LZtW6+x3/72tzp48KCk4Pp3es6BAwf04Ycf6rHHHvOMBVuff/zjHzVlyhQ99NBD6tChgx555BE988wzyszMlOS/fgk0lxEaGqrOnTsrNzfXazw3N1c9evRwqKqrLyEhQdHR0V59nz59Wnl5eVb2bYzR+PHjtWLFCm3YsEEJCQlex4Ot318zxqiysjLoerznnntUUFCg/Px8z5acnKw//OEPys/P18033xxU/f5aZWWlvvjiC8XExATVz/WOO+6otpzCl19+6fkw4mDq9ZycnBxFRkZqwIABnrFg6/PHH3/Uddd5x4169ep5Htv2W7++37d87Tj32PaCBQvM559/biZOnGgaNWpk9u/f73RpV+TEiRNm9+7dZvfu3UaSycrKMrt37/Y8jj537lwTERFhVqxYYQoKCszDDz9s7WODTzzxhImIiDCbNm3yelTyxx9/9JwTDP1OnTrVbN682RQVFZk9e/aYadOmmeuuu86sX7/eGBMcPV7Kr59yMiZ4+p00aZLZtGmT+eabb8y2bdvM7373OxMeHu75HRQsfW7fvt2EhISY2bNnm8LCQvPWW2+Zhg0bmjfffNNzTrD0aowxZ8+eNS1atDDPPfdctWPB1OeIESPMb37zG89j2ytWrDDNmjUzzz77rOccf/RLoKmhv/71ryY+Pt6Ehoaa2267zfO4r802btxoJFXbRowYYYz55VG6GTNmmOjoaON2u81dd91lCgoKnC3aRxfqU5LJycnxnBMM/Y4aNcrz9/Smm24y99xzjyfMGBMcPV7K+YEmWPo9tyZH/fr1TWxsrElLSzN79+71HA+WPo0x5r//+79N+/btjdvtNm3atDF/+9vfvI4HU6/r1q0zksy+ffuqHQumPsvLy82ECRNMixYtTIMGDczNN99spk+fbiorKz3n+KNflzHG+DqNBAAAEAi4hwYAAFiPQAMAAKxHoAEAANYj0AAAAOsRaAAAgPUINAAAwHoEGgAAYD0CDQAAsB6BBgAAWI9AAyAgbd26VfXq1VO/fv2cLgWABfjoAwAB6bHHHlPjxo31xhtv6PPPP1eLFi2cLglAAGOGBkDAqaio0DvvvKMnnnhCv/vd77Rw4UKv46tWrVJiYqLCwsLUp08fLVq0SC6XS8eOHfOcs3XrVt11110KCwtTXFycnn76aVVUVNRtIwDqDIEGQMBZtmyZWrdurdatW2vYsGHKycnRucnk/fv36/e//70GDRqk/Px8jRkzRtOnT/d6fUFBge677z6lpaVpz549WrZsmbZs2aLx48c70Q6AOsBbTgACzh133KF//dd/1YQJE3TmzBnFxMTo7bff1r333qspU6Zo9erVKigo8Jz/pz/9SbNnz9bRo0d1/fXXa/jw4QoLC9Nrr73mOWfLli3q1auXKioq1KBBAyfaAnAVMUMDIKDs27dP27dv10MPPSRJCgkJ0ZAhQ/T3v//dc7xLly5er7n99tu99nft2qWFCxeqcePGnu2+++5TVVWVioqK6qYRAHUqxOkCAODXFixYoDNnzug3v/mNZ8wYo/r16+vo0aMyxsjlcnm95vyJ5qqqKo0ZM0ZPP/10tetzczEQnAg0AALGmTNntHjxYr344otKSUnxOvbAAw/orbfeUps2bbRmzRqvYzt37vTav+2227R37161atXqqtcMIDBwDw2AgPH+++9ryJAhKisrU0REhNex6dOna82aNVqxYoVat26tZ555RqNHj1Z+fr4mTZqkQ4cO6dixY4qIiNCePXvUrVs3Pfroo3r88cfVqFEjffHFF8rNzdV//ud/OtQdgKuJe2gABIwFCxbo3nvvrRZmpF9maPLz83X06FG99957WrFihTp27Kjs7GzPU05ut1uS1LFjR+Xl5amwsFB33nmnbr31Vj3//POKiYmp034A1B1maABYb/bs2Xr11VdVXFzsdCkAHMI9NACs88orr6hLly668cYb9fHHH2vevHmsMQNc4wg0AKxTWFioWbNm6YcfflCLFi00adIkTZ061emyADiIt5wAAID1uCkYAABYj0ADAACsR6ABAADWI9AAAADrEWgAAID1CDQAAMB6BBoAAGA9Ag0AALDe/wPx0CBCaW477wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_pop3= pd.read_csv(\"data/ages_population3.csv\")\n",
+    "\n",
+    "freq_ages3 = ages_pop3[\"observation\"].value_counts()\n",
+    "\n",
+    "plt.bar(freq_ages3.index, freq_ages3.values)\n",
+    "plt.xlabel(\"Age\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean 1: 36.56\n",
+      "Standard deviation 1: 12.816499625976762\n",
+      "Mean 3:41.989\n",
+      "Standard deviation 3 :16.144705959865934\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "mean = ages_pop[\"observation\"].mean()\n",
+    "print(f\"Mean 1: {mean}\")\n",
+    "\n",
+    "std_dev = ages_pop[\"observation\"].std()\n",
+    "print(f\"Standard deviation 1: {std_dev}\")\n",
+    "\n",
+    "mean = ages_pop3[\"observation\"].mean()\n",
+    "print(f\"Mean 3:{mean}\")\n",
+    "\n",
+    "std_dev = ages_pop3[\"observation\"].std()\n",
+    "print(f\"Standard deviation 3 :{std_dev}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Q1: 30.0\n",
+      "Q2 40.0\n",
+      "Q3: 53.0\n",
+      "Q4: 77.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "q1 = np.quantile(ages_pop3, 0.25)\n",
+    "print(f\"Q1: {q1}\")\n",
+    "q2 = np.quantile(ages_pop3, 0.50)\n",
+    "print(f\"Q2 {q2}\")\n",
+    "q3 = np.quantile(ages_pop3, 0.75)\n",
+    "print(f\"Q3: {q3}\")\n",
+    "q4 = np.quantile(ages_pop3, 1)\n",
+    "print(f\"Q4: {q4}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p10 = np.percentile(ages_population_3, 10)\n",
+    "p90 = np.percentile(ages_population_3, 90)\n",
+    "\n",
+    "print(f\"The 10th percentile is {p10}\")\n",
+    "print(f\"The 90th percentile is {p90}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus challenge\n",
+    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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98LDDe+65R2VlZXruuefOeq3evXt7PSzwmmuu0ZVXXql58+bp2Wef9WncNVHd2O6++27PvxMTE9WzZ0/FxcVp7dq1Zy04B8trJkl//vOfdd99950130uB85rVx+9WgfB6VTeuikX0YHyPnW1cwf7+gj1C9fUPtnEF6/y8XLDO0ysK5jl7ZcE+hy8XKnP5yoJtbl+Rv+b53MKlGq1atVKjRo2q/KW7oKCgyl8tAl35k40DeSxjxozRmjVrtGHDBrVp08bTHgyxN2nSRJdffrl69uypzMxMdevWTc8880zAx759+3YVFBSoR48eCgsLU1hYmLKzs/Xss88qLCzME2Ogxl9R8+bN1aVLF+3duzfgv++SFBMTU2XF2Y9//GMdOHBAUnD83O/fv19vv/22fv7zn3vaAj3uxx9/XBMnTtQ999yjLl266IEHHtCvfvUrZWZmSgr8+BE4fJ2zXC5XQPzcVTeucqdOndJdd92l3NxcZWVlnXVlrJULLrhAV111lfbu3eurkGvsXGOrKCYmRnFxcWeNM1heM0n6xz/+oT179nj9f11Tdr1mvv7dKlBer+rGVS5Y32PnGldFwfT+gv+F0hy8omD8HTOY5+flgnWeXlEozdkrC7Y5fLlQmMtXFoxz+4r8Nc+ngF6NJk2aqEePHsrKyvJqz8rKUt++fW2Kqm7i4+Plcrm8xnLy5EllZ2fbPhZjjEaPHq2VK1fq3XffVXx8vNfxQI69OsYYlZaWBnzsAwYM0K5du5STk+PZevbsqfvuu085OTm67LLLAjr+ikpLS/Xpp58qJiYm4L/v0g8rxPbs2ePV9tlnnykuLk5ScPzcL1q0SK1bt1ZqaqqnLdDjPnbsmC64wDvtNWrUSGVlZZICP37Yr75yVp8+far8vrF+/Xq//dyda1zSfwt7e/fu1dtvv62WLVvW6evk5OQoJibGF2HX+Guea2yVHTp0SHl5eWeNMxhes3ILFy5Ujx491K1btzp9HX+/ZtXFcT6/W9n9elWnfFxS8L7HqoujfFyVBcP7C/YJpTl4RcH0O2Yozs/LBcs8vaJQmrNXFmxz+HKhMJevLBjn9hX5bZ5f48eNNkDLly83jRs3NgsXLjSffPKJSU9PN82bNzf79u2zO7Qqjhw5Ynbs2GF27NhhJJk5c+aYHTt2mP379xtjjJk5c6aJiooyK1euNLt27TL33nuviYmJMcXFxbbG/ctf/tJERUWZjRs3moMHD3q2Y8eOefoEauzGGDNp0iTz3nvvmdzcXLNz504zefJkc8EFF5j169cbYwI7disVn+htTODGP378eLNx40bz5Zdfmi1btphbbrnFREREeN6bgRp3uQ8++MCEhYWZGTNmmL1795qXX37ZNGvWzCxdutTTJ5DHcObMGdOuXTvzxBNPVDkWyHGnpaWZSy+91Lz++usmNzfXrFy50rRq1cpMmDDB0yeQ44f9fJWzHnjgATNx4kTP/j//+U/TqFEjM3PmTPPpp5+amTNnmrCwMLNly5aAGNepU6fMrbfeatq0aWNycnK8+pSWllY7LrfbbdatW2e++OILs2PHDvPggw+asLAw869//csv46rJ2I4cOWLGjx9vNm/ebHJzc82GDRtMnz59zKWXXhrUr1m5oqIi06xZM7NgwQLL6wTia+aL360C7fU617iC+T12tnEF6/sL9gqmOXhFwTofryzY5+flQm2eXlGwzNkrC/Y5fLlgn8tXFqxz+4r8Nc+ngH4Of/jDH0xcXJxp0qSJufLKK012drbdIVnasGGDkVRlS0tLM8YYU1ZWZqZOnWpcLpdxOp3m+uuvN7t27bI3aGMsY5ZkFi1a5OkTqLEbY8xDDz3k+fm45JJLzIABAzxJ2ZjAjt1K5WQcqPHffffdJiYmxjRu3NjExsaaIUOGmN27d3uOB2rcFf3v//6vSUxMNE6n01xxxRXmhRde8DoeyGN46623jCSzZ8+eKscCOe7i4mIzduxY065dO9O0aVNz2WWXmSlTpngVJwI5ftjPVzmrX79+nvxc7tVXXzUdO3Y0jRs3NldccYV57bXX/DCiH5xrXLm5udX22bBhQ7XjSk9PN+3atfPkyOTkZLN582a/jasmYzt27JhJTk42l1xyiWncuLFp166dSUtLMwcOHPC6TrC9ZuX++Mc/mvDwcHP48GHL6wTia+aL360C7fUy5uzjCub32NnGFazvL9gvWObgFQXrfLyyYJ+flwu1eXpFwTJnrywU5vDlgnkuX1mwzu0r8tc832GMMTVfrw4AAAAAAAAAQMPAPdABAAAAAAAAALBAAR0AAAAAAAAAAAsU0AEAAAAAAAAAsEABHQAAAAAAAAAACxTQAQAAAAAAAACwQAEdAAAAAAAAAAALFNABAAAAAAAAALBAAR0AAAAAAAAAAAsU0AEAAAAAAAAAsEABHYDH5s2b1ahRI9100012hwIAAGqI/A0AQHAihwPBwWGMMXYHASAw/PznP9eFF16oP/3pT/rkk0/Url07u0MCAADnQP4GACA4kcOB4MAKdACSpJKSEv31r3/VL3/5S91yyy1avHix1/E1a9YoISFB4eHh6t+/v5YsWSKHw6HDhw97+mzevFnXX3+9wsPD1bZtWz366KMqKSnx70AAAGhAyN8AAAQncjgQPCigA5AkrVixQh07dlTHjh11//33a9GiRSr/gMq+ffv0s5/9TLfffrtycnI0YsQITZkyxev8Xbt26cYbb9SQIUO0c+dOrVixQps2bdLo0aPtGA4AAA0C+RsAgOBEDgeCB7dwASBJuuaaa3TXXXdp7NixOn36tGJiYvTKK69o4MCBmjhxotauXatdu3Z5+v/617/WjBkzVFhYqIsuukjDhg1TeHi4/vjHP3r6bNq0Sf369VNJSYmaNm1qx7AAAAhp5G8AAIITORwIHqxAB6A9e/bogw8+0D333CNJCgsL0913360///nPnuNXXXWV1zlXX3211/727du1ePFiXXjhhZ7txhtvVFlZmXJzc/0zEAAAGhDyNwAAwYkcDgSXMLsDAGC/hQsX6vTp07r00ks9bcYYNW7cWIWFhTLGyOFweJ1T+cMrZWVlGjFihB599NEq1+dBKAAA+B75GwCA4EQOB4ILBXSggTt9+rReeuklPf3000pOTvY69tOf/lQvv/yyrrjiCr3xxhtex7Zt2+a1f+WVV2r37t26/PLL6z1mAAAaOvI3AADBiRwOBB/ugQ40cKtXr9bdd9+tgoICRUVFeR2bMmWK3njjDa1cuVIdO3bUr371Kz388MPKycnR+PHj9dVXX+nw4cOKiorSzp071bt3bz344IN65JFH1Lx5c3366afKysrSvHnzbBodAAChifwNAEBwIocDwYd7oAMN3MKFCzVw4MAqiVv64a/fOTk5Kiws1N/+9jetXLlSXbt21YIFCzxPAHc6nZKkrl27Kjs7W3v37tV1112n7t2768knn1RMTIxfxwMAQENA/gYAIDiRw4Hgwwp0AHUyY8YMPf/888rLy7M7FAAAUEPkbwAAghM5HLAP90AHUCPPPfecrrrqKrVs2VL//Oc/9dRTT2n06NF2hwUAAM6C/A0AQHAihwOBgwI6gBrZu3evpk+fru+//17t2rXT+PHjNWnSJLvDAgAAZ0H+BgAgOJHDgcDBLVwAAAAAAAAAALDAQ0QBAAAAAAAAALBAAR0AAAAAAAAAAAsU0AEAAAAAAAAAsEABHQAAAAAAAAAACxTQAQAAAAAAAACwQAEdAAAAAAAAAAALFNABAAAAAAAAALBAAR0AAAAAAAAAAAsU0AEAAAAAAAAAsPD/AUotTs/G/PK4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "\n",
+    "# Plot for Population 1\n",
+    "freq_distribution = ages_pop[\"observation\"].value_counts()\n",
+    "axes[0].bar(freq_ages.index, freq_ages.values)\n",
+    "axes[0].set_xlabel(\"Age\")\n",
+    "axes[0].set_ylabel(\"Frequency\")\n",
+    "axes[0].set_title(\"Population 1\")\n",
+    "\n",
+    "# Plot for Population 2\n",
+    "freq_distribution = ages_pop2[\"observation\"].value_counts()\n",
+    "axes[1].bar(ages_pop2_freq.index, ages_pop2_freq.values)\n",
+    "axes[1].set_xlabel(\"Age\")\n",
+    "axes[1].set_ylabel(\"Frequency\")\n",
+    "axes[1].set_title(\"Population 2\")\n",
+    "\n",
+    "# Plot for Population 3\n",
+    "freq_distribution = ages_pop[\"observation\"].value_counts()\n",
+    "axes[2].bar(freq_ages3.index, freq_ages3.values)\n",
+    "axes[2].set_xlabel(\"Age\")\n",
+    "axes[2].set_ylabel(\"Frequency\")\n",
+    "axes[2].set_title(\"Population 3\")\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/ages_population.csv b/your-code/data/ages_population.csv
similarity index 100%
rename from data/ages_population.csv
rename to your-code/data/ages_population.csv
diff --git a/data/ages_population2.csv b/your-code/data/ages_population2.csv
similarity index 100%
rename from data/ages_population2.csv
rename to your-code/data/ages_population2.csv
diff --git a/data/ages_population3.csv b/your-code/data/ages_population3.csv
similarity index 100%
rename from data/ages_population3.csv
rename to your-code/data/ages_population3.csv
diff --git a/data/roll_the_dice_hundred.csv b/your-code/data/roll_the_dice_hundred.csv
similarity index 100%
rename from data/roll_the_dice_hundred.csv
rename to your-code/data/roll_the_dice_hundred.csv
diff --git a/data/roll_the_dice_thousand.csv b/your-code/data/roll_the_dice_thousand.csv
similarity index 100%
rename from data/roll_the_dice_thousand.csv
rename to your-code/data/roll_the_dice_thousand.csv
diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..e7e2a8e 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,11 +11,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "# Libraries\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import random\n",
+    "import seaborn as sns"
    ]
   },
   {
@@ -29,11 +34,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 6, 3, 1, 6, 3, 2, 2, 1, 2]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def roll_dice_trials(k):\n",
+    "    mylist = np.arange(1, 7)\n",
+    "    return random.choices(mylist, k=k)\n",
+    "\n",
+    "dice_10 = roll_dice_trials(10)\n",
+    "dice_10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Results</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Trials</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Results\n",
+       "Trials         \n",
+       "1             5\n",
+       "2             6\n",
+       "3             3\n",
+       "4             1\n",
+       "5             6\n",
+       "6             3\n",
+       "7             2\n",
+       "8             2\n",
+       "9             1\n",
+       "10            2"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "dice_df = pd.DataFrame(dice_10, columns=[\"Results\"])\n",
+    "dice_df.index = range(1, len(dice_df) + 1)\n",
+    "dice_df.index.name = \"Trials\"\n",
+    "dice_df"
    ]
   },
   {
@@ -45,11 +174,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "dice = np.sort(dice_df)\n",
+    "plt.hist(dice_df)\n",
+    "plt.show()"
    ]
   },
   {
@@ -61,18 +204,103 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 153,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method IndexOpsMixin.value_counts of Trials\n",
+       "1     5\n",
+       "2     6\n",
+       "3     3\n",
+       "4     1\n",
+       "5     6\n",
+       "6     3\n",
+       "7     2\n",
+       "8     2\n",
+       "9     1\n",
+       "10    2\n",
+       "Name: Results, dtype: int32>"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "freq= dice_df[\"Results\"].value_counts\n",
+    "freq"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Ana\\AppData\\Local\\Temp\\ipykernel_9896\\400507941.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(freq)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(freq)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "your comments here\n",
@@ -91,11 +319,148 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Results</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Trials</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Results\n",
+       "Trials         \n",
+       "1             5\n",
+       "2             6\n",
+       "3             3\n",
+       "4             1\n",
+       "5             6\n",
+       "6             3\n",
+       "7             2\n",
+       "8             2\n",
+       "9             1\n",
+       "10            2"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "dice_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(3+6+6+2+4+4+4+4+6+1)/10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.1"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "def cal_mean(column):\n",
+    "    return sum(column)/len(column)\n",
+    "\n",
+    "cal_mean(dice_df[\"Results\"])"
    ]
   },
   {
@@ -107,11 +472,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 158,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1, 2, 3, 5, 6]), array([2, 3, 2, 1, 2], dtype=int64))"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "fq = np.unique(dice_df, return_counts=True)\n",
+    "unique_values = fq[0]\n",
+    "frequencies = fq[1]\n",
+    "fq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.1"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "total_values = np.sum(frequencies)\n",
+    "sum_products = np.sum(unique_values * frequencies)\n",
+    "\n",
+    "mean = sum_products / total_values\n",
+    "mean"
    ]
   },
   {
@@ -124,11 +528,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def calculate_median(data):\n",
+    "    sorted_data = np.sort(data)\n",
+    "    num_observations = len(sorted_data)\n",
+    "\n",
+    "    if num_observations % 2 == 1:  \n",
+    "        middle_index = num_observations // 2\n",
+    "        median = sorted_data[middle_index]\n",
+    "    else:  # Even number of observations\n",
+    "        middle_index_1 = (num_observations - 1) // 2\n",
+    "        middle_index_2 = num_observations // 2\n",
+    "        median = (sorted_data[middle_index_1] + sorted_data[middle_index_2]) / 2\n",
+    "\n",
+    "    return median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4.5])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calculate_median(dice_df)"
    ]
   },
   {
@@ -140,11 +578,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def calculate_quartiles(data):\n",
+    "    sorted_data = np.sort(data)\n",
+    "    num_observations = len(sorted_data)\n",
+    "    q1_index = int(num_observations * 0.25)\n",
+    "    \n",
+    "    q1 = calculate_median(sorted_data[:q1_index + 1])\n",
+    "    \n",
+    "    q2 = calculate_median(sorted_data)\n",
+    "    \n",
+    "    q3_index = int(num_observations * 0.75)\n",
+    "    \n",
+    "    q3 = calculate_median(sorted_data[q3_index:])\n",
+    "\n",
+    "    return q1, q2, q3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([6]), array([4.5]), array([1]))"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = dice_df\n",
+    "calculate_quartiles(dice_df)"
    ]
   },
   {
@@ -158,11 +631,301 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>95</td>\n",
+       "      <td>95</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>96</td>\n",
+       "      <td>96</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>97</td>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>98</td>\n",
+       "      <td>98</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  roll  value\n",
+       "0            0     0      1\n",
+       "1            1     1      2\n",
+       "2            2     2      6\n",
+       "3            3     3      1\n",
+       "4            4     4      6\n",
+       "..         ...   ...    ...\n",
+       "95          95    95      4\n",
+       "96          96    96      6\n",
+       "97          97    97      1\n",
+       "98          98    98      3\n",
+       "99          99    99      6\n",
+       "\n",
+       "[100 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "roll_dice = pd.read_csv(\"data/roll_the_dice_hundred.csv\")\n",
+    "roll_dice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>47</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>56</td>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>73</td>\n",
+       "      <td>73</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>24</td>\n",
+       "      <td>24</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>21</td>\n",
+       "      <td>21</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  roll  value\n",
+       "0            0     0      1\n",
+       "47          47    47      1\n",
+       "56          56    56      1\n",
+       "9            9     9      1\n",
+       "73          73    73      1\n",
+       "..         ...   ...    ...\n",
+       "17          17    17      6\n",
+       "11          11    11      6\n",
+       "24          24    24      6\n",
+       "21          21    21      6\n",
+       "99          99    99      6\n",
+       "\n",
+       "[100 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "roll_sort = roll_dice.sort_values(by = \"value\")\n",
+    "roll_sort"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Ana\\AppData\\Local\\Temp\\ipykernel_9896\\1384629514.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(roll_sort)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(roll_sort)\n",
+    "plt.show()"
    ]
   },
   {
@@ -185,11 +948,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 105,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def cal_mean(column):\n",
+    "    return sum(column)/len(column)\n",
+    "\n",
+    "cal_mean(roll_dice[\"value\"])"
    ]
   },
   {
@@ -201,11 +979,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 114,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6    23\n",
+       "4    22\n",
+       "2    17\n",
+       "3    14\n",
+       "1    12\n",
+       "5    12\n",
+       "Name: value, dtype: int64"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "fq = roll_dice[\"value\"].value_counts()\n",
+    "\n",
+    "fq"
    ]
   },
   {
@@ -217,11 +1015,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 118,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x2341fa105b0>"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "plt.hist(roll_dice[\"value\"])\n",
+    "x = data[\"value\"]\n",
+    "plt.axvline(x.mean(), color = \"pink\")"
    ]
   },
   {
@@ -244,11 +1066,185 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 98,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>995</td>\n",
+       "      <td>995</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>996</td>\n",
+       "      <td>996</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>997</td>\n",
+       "      <td>997</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>998</td>\n",
+       "      <td>998</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>999</td>\n",
+       "      <td>999</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  roll  value\n",
+       "0             0     0      5\n",
+       "1             1     1      6\n",
+       "2             2     2      1\n",
+       "3             3     3      6\n",
+       "4             4     4      5\n",
+       "..          ...   ...    ...\n",
+       "995         995   995      1\n",
+       "996         996   996      4\n",
+       "997         997   997      4\n",
+       "998         998   998      3\n",
+       "999         999   999      6\n",
+       "\n",
+       "[1000 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "thousand = pd.read_csv(\"data/roll_the_dice_thousand.csv\")\n",
+    "thousand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6    23\n",
+       "4    22\n",
+       "2    17\n",
+       "3    14\n",
+       "1    12\n",
+       "5    12\n",
+       "Name: value, dtype: int64"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fq_thousand = roll_dice[\"value\"].value_counts()\n",
+    "fq_thousand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "plt.bar(fq_thousand.index, fq_thousand.values, color=\"lemonchiffon\")\n",
+    "plt.xlabel(\"Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -274,11 +1270,166 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 160,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>53.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     observation\n",
+       "0           68.0\n",
+       "1           12.0\n",
+       "2           45.0\n",
+       "3           38.0\n",
+       "4           49.0\n",
+       "..           ...\n",
+       "995         27.0\n",
+       "996         47.0\n",
+       "997         53.0\n",
+       "998         33.0\n",
+       "999         31.0\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_pop = pd.read_csv(\"data/ages_population.csv\")\n",
+    "ages_pop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39.0    45\n",
+       "41.0    36\n",
+       "30.0    34\n",
+       "35.0    33\n",
+       "43.0    32\n",
+       "        ..\n",
+       "73.0     1\n",
+       "82.0     1\n",
+       "70.0     1\n",
+       "71.0     1\n",
+       "69.0     1\n",
+       "Name: observation, Length: 72, dtype: int64"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "freq_ages = ages_pop[\"observation\"].value_counts()\n",
+    "freq_ages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(freq_ages.index, freq_ages.values, color=\"salmon\")\n",
+    "plt.xlabel(\"Age\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -290,11 +1441,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 139,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 36.56\n",
+      "Standard deviation: 12.816499625976762\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "mean = ages_pop[\"observation\"].mean()\n",
+    "print(f\"Mean: {mean}\")\n",
+    "\n",
+    "std_dev = ages_pop[\"observation\"].std()\n",
+    "print(f\"Standard deviation: {std_dev}\")"
    ]
   },
   {
@@ -317,11 +1482,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 144,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "ages_pop2= pd.read_csv(\"data/ages_population2.csv\")\n",
+    "ages_pop2_freq= ages_pop2[\"observation\"].value_counts()\n",
+    "\n",
+    "plt.bar(ages_pop2_freq.index, ages_pop2_freq.values)\n",
+    "plt.xlabel(\"Age\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -351,11 +1534,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 146,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean 1: 36.56\n",
+      "Standard deviation 1: 12.816499625976762\n",
+      "Mean 2:27.155\n",
+      "Standard deviation 2 :2.969813932689186\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "mean = ages_pop[\"observation\"].mean()\n",
+    "print(f\"Mean 1: {mean}\")\n",
+    "\n",
+    "std_dev = ages_pop[\"observation\"].std()\n",
+    "print(f\"Standard deviation 1: {std_dev}\")\n",
+    "\n",
+    "mean = ages_pop2[\"observation\"].mean()\n",
+    "print(f\"Mean 2:{mean}\")\n",
+    "\n",
+    "std_dev = ages_pop2[\"observation\"].std()\n",
+    "print(f\"Standard deviation 2 :{std_dev}\")\n",
+    "\n"
    ]
   },
   {
@@ -381,11 +1587,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 148,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "ages_pop3= pd.read_csv(\"data/ages_population3.csv\")\n",
+    "\n",
+    "freq_ages3 = ages_pop3[\"observation\"].value_counts()\n",
+    "\n",
+    "plt.bar(freq_ages3.index, freq_ages3.values)\n",
+    "plt.xlabel(\"Age\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -397,11 +1622,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 149,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean 1: 36.56\n",
+      "Standard deviation 1: 12.816499625976762\n",
+      "Mean 3:41.989\n",
+      "Standard deviation 3 :16.144705959865934\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "mean = ages_pop[\"observation\"].mean()\n",
+    "print(f\"Mean 1: {mean}\")\n",
+    "\n",
+    "std_dev = ages_pop[\"observation\"].std()\n",
+    "print(f\"Standard deviation 1: {std_dev}\")\n",
+    "\n",
+    "mean = ages_pop3[\"observation\"].mean()\n",
+    "print(f\"Mean 3:{mean}\")\n",
+    "\n",
+    "std_dev = ages_pop3[\"observation\"].std()\n",
+    "print(f\"Standard deviation 3 :{std_dev}\")"
    ]
   },
   {
@@ -424,11 +1671,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 151,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Q1: 30.0\n",
+      "Q2 40.0\n",
+      "Q3: 53.0\n",
+      "Q4: 77.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "q1 = np.quantile(ages_pop3, 0.25)\n",
+    "print(f\"Q1: {q1}\")\n",
+    "q2 = np.quantile(ages_pop3, 0.50)\n",
+    "print(f\"Q2 {q2}\")\n",
+    "q3 = np.quantile(ages_pop3, 0.75)\n",
+    "print(f\"Q3: {q3}\")\n",
+    "q4 = np.quantile(ages_pop3, 1)\n",
+    "print(f\"Q4: {q4}\")"
    ]
   },
   {
@@ -455,7 +1721,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "p10 = np.percentile(ages_population_3, 10)\n",
+    "p90 = np.percentile(ages_population_3, 90)\n",
+    "\n",
+    "print(f\"The 10th percentile is {p10}\")\n",
+    "print(f\"The 90th percentile is {p90}\")"
    ]
   },
   {
@@ -479,11 +1749,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 162,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "\n",
+    "# Plot for Population 1\n",
+    "freq_distribution = ages_pop[\"observation\"].value_counts()\n",
+    "axes[0].bar(freq_ages.index, freq_ages.values)\n",
+    "axes[0].set_xlabel(\"Age\")\n",
+    "axes[0].set_ylabel(\"Frequency\")\n",
+    "axes[0].set_title(\"Population 1\")\n",
+    "\n",
+    "# Plot for Population 2\n",
+    "freq_distribution = ages_pop2[\"observation\"].value_counts()\n",
+    "axes[1].bar(ages_pop2_freq.index, ages_pop2_freq.values)\n",
+    "axes[1].set_xlabel(\"Age\")\n",
+    "axes[1].set_ylabel(\"Frequency\")\n",
+    "axes[1].set_title(\"Population 2\")\n",
+    "\n",
+    "# Plot for Population 3\n",
+    "freq_distribution = ages_pop[\"observation\"].value_counts()\n",
+    "axes[2].bar(freq_ages3.index, freq_ages3.values)\n",
+    "axes[2].set_xlabel(\"Age\")\n",
+    "axes[2].set_ylabel(\"Frequency\")\n",
+    "axes[2].set_title(\"Population 3\")\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
@@ -500,9 +1807,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ironhack-3.7",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "ironhack-3.7"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -514,7 +1821,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,